Meta-analysis results

RMA results with model-based SEs k = number of studies; sqrt in “Variance components” = tau, the standard deviation of true effects; estimate in “Model results” = naive MA estimate

RVE SEs with Satterthwaite small-sample correction Estimate based on a multilevel RE model with constant sampling correlation model (CHE - correlated hierarchical effects - working model) (Pustejovsky & Tipton, 2020; https://osf.io/preprints/metaarxiv/vyfcj/). Interpretation of naive-meta-analysis should be based on these estimates.

Prediction interval Shows the expected range of true effects in similar studies. As an approximation, in 95% of cases the true effect in a new published study can be expected to fall between PI LB and PI UB. Note that these are non-adjusted estimates. An unbiased newly conducted study will more likely fall in an interval centered around bias-adjusted estimate with a wider CI width.

Heterogeneity Tau can be interpreted as the total amount of heterogeneity in the true effects. I^2$ represents the ratio of true heterogeneity to total variance across the observed effect estimates. Estimates calculated by 2 approaches are reported. This is followed by separate estimates of between- and within-cluster heterogeneity and estimated intra-class correlation of underlying true effects.

Proportion of significant results What proportion of effects were statistically at the alpha level of .05.

ES-precision correlation Kendalls’s correlation between the ES and precision

4/3PSM Applies a permutation-based, step-function 4-parameter selection model (one-tailed p-value steps = c(.025, .5, 1)). Falls back to 3-parameter selection model if at least one of the three p-value intervals contains less than 5 p-values.

pvalue = p-value testing H0 that the effect is zero. ciLB and ciUB are lower and upper bound of the CI. k = number of studies. steps = 3 means that the 4PSM was applied, 2 means that the 3PSM was applied. For this meta-analysis, we applied 3-parameter selection model by default as there were only 11 independent effects in the opposite direction overall (6%), causing the estimates to be unstable across iterations.

PET-PEESE Estimated effect size of an infinitely precise study. Using 4/3PSM as the conditional estimator instead of PET (can be changed to PET). If the PET-PEESE estimate is in the opposite direction, the effect can be regarded nil. By default (can be changed to PET), the function employs a modified sample-size based estimator (see https://www.jepusto.com/pet-peese-performance/). It also uses the same RVE sandwich-type based estimator in a CHE (correlated hierarchical effects) working model with the identical random effects structure as the primary (naive) meta-analytic model.

We report results for both, PET and PEESE, with the first reported one being the primary (based on the conditional estimator).

WAAP-WLS The combined WAAP-WLS estimator (weighted average of the adequately powered - weighted least squares) tries to identify studies that are adequately powered to detect the meta-analytic effect. If there is less than two such studies, the method falls back to the WLS estimator (Stanley & Doucouliagos, 2015). If there are at least two adequately powered studies, WAAP returns a WLS estimate based on effects from only those studies.

type = 1: WAAP estimate, 2: WLS estimate. kAdequate = number of adequately powered studies

p-uniform P-uniform* is a selection model conceptually similar to p-curve. It makes use of the fact that p-values follow a uniform distribution at the true effect size while it includes also nonsignificant effect sizes. Permutation-based new version of p-uniform method, the so-called p-uniform* (van Aert, van Assen, 2021).

p-curve Permutation-based p-curve method. Output should be pretty self-explanatory.

Power for detecting SESOI and bias-corrected parameter estimates Estimates of the statistical power for detecting a smallest effect sizes of interest equal to .20, .50, and .70 in SD units (Cohen’s d). A sort of a thought experiment, we also assumed that population true values equal the bias-corrected estimates (4/3PSM or PET-PEESE) and computed power for those.

Handling of dependencies in bias-correction methods To handle dependencies among the effects, the 4PSM, p-curve, p-uniform are implemented using a permutation-based procedure, randomly selecting only one focal effect (i.e., excluding those which were not coded as being focal) from a single study and iterating nIterations times. Lastly, the procedure selects the result with the median value of the ES estimate (4PSM, p-uniform) or median z-score of the full p-curve (p-curve).

Directionality of social thermoregulation effects

Source target directionality

## $`Model results`
## $`Model results`$test
##                                          Coef. Estimate     SE t-stat p-val (z)
## 1 factor(sourceTargetDirectionality_reconcil)0    0.465 0.0424  10.97    <0.001
## 2 factor(sourceTargetDirectionality_reconcil)1    0.421 0.0715   5.88    <0.001
##   Sig.
## 1  ***
## 2  ***
## 
## $`Model results`$CIs
##                                           Coef Estimate     SE d.f.
## 1 factor(sourceTargetDirectionality_reconcil)0    0.465 0.0424  Inf
## 2 factor(sourceTargetDirectionality_reconcil)1    0.421 0.0715  Inf
##   Lower 95% CI Upper 95% CI
## 1        0.382        0.548
## 2        0.281        0.561
## 
## 
## $`RVE Wald test`
##  test Fstat df_num df_denom p_val sig
##   HTZ 0.277      1     39.2 0.602

Moderation by prior experiences in relationships

Forest plot

Funnel plot

P-curve plot

Effect type

## [1] "The compensatory vs priming effects conceptualized by the actual direction of the effect as contrast vs. assimilation"

Compensatory

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 45; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.1133  0.3366     35     no         study 
## sigma^2.2  0.0068  0.0826     45     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 44) = 266.6482, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.2364  0.0659  3.5884  0.0003  0.1073  0.3655  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.236 0.0659   3.59   33      0.00106   **
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.236 0.0659   33        0.102         0.37
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.480     0.953 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.3465356                    87.8083032 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    92.4000000                    82.8200000 
##  Within-cluster heterogeneity                           ICC 
##                     4.9800000                     0.9400000 
## 
## $`Proportion of significant results`
## [1] 0.5106383
## 
## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.6897864
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.084  0.098  0.855  0.393 -0.109  0.277 35.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
##   PET estimate             se         zvalue         pvalue           ciLB 
##         -0.295          0.167         -1.767          0.086         -0.634 
##           ciUB PEESE estimate             se         zvalue         pvalue 
##          0.045          0.032          0.097          0.328          0.745 
##           ciLB           ciUB 
##         -0.165          0.229 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term estimate  std.error  statistic    p.value   conf.low conf.high
## 1 WAAP-WLS   b0  0.14467 0.04490171 0.04490171 0.00239815 0.05417654 0.2351635
##   type kAdequate
## 1    2         1
## 
## $`Publication bias`$`p-uniform*`
##         est        ciLB        ciUB      pvalue 
## -0.01100584 -0.19044259  0.17447474  0.90604429 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 52 
## - Total number of p<0.05 studies included into the analysis: k = 36 (69.23%) 
## - Total number of studies with p<0.025: k = 25 (48.08%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.014 -6.934 0.000 -6.997     0
## Flatness test           0.459  3.037 0.999  9.525     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 68% (49.9%-81.1%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##              Median power for detecting a SESOI of d = .20 
##                                                    "0.385" 
##              Median power for detecting a SESOI of d = .50 
##                                                    "0.986" 
##              Median power for detecting a SESOI of d = .70 
##                                                        "1" 
## Median power for detecting PET-PEESE estimate.PET estimate 
##                    "ES estimate in the opposite direction" 
##             Median power for detecting 4/3PSM estimate.est 
##                                                    "0.108"

Priming

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 125; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0347  0.1864     84     no         study 
## sigma^2.2  0.0196  0.1399    125     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 124) = 315.2924, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.4497  0.0339  13.2675  <.0001  0.3833  0.5162  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt     0.45 0.0339   13.3 73.9       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt     0.45 0.0339 73.9        0.382        0.517
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.019     0.918 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.2330976                    60.9134712 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    76.8600000                    38.9600000 
##  Within-cluster heterogeneity                           ICC 
##                    21.9600000                     0.6400000 
## 
## $`Proportion of significant results`
## [1] 0.6136364
## 
## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.5434097
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.248  0.052  4.780  0.000  0.147  0.350 83.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
## PEESE estimate             se         zvalue         pvalue           ciLB 
##          0.204          0.056          3.656          0.000          0.093 
##           ciUB   PET estimate             se         zvalue         pvalue 
##          0.315         -0.049          0.060         -0.810          0.421 
##           ciLB           ciUB 
##         -0.169          0.071 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term   estimate  std.error  statistic   p.value    conf.low
## 1 WAAP-WLS   b0 0.06033676 0.03493944 0.03493944 0.1349326 -0.02515696
##   conf.high type kAdequate
## 1 0.1458305    1         7
## 
## $`Publication bias`$`p-uniform*`
##           est          ciLB          ciUB        pvalue 
## 0.28237593329 0.17999288439 0.38133351331 0.00001748352 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 116 
## - Total number of p<0.05 studies included into the analysis: k = 80 (68.97%) 
## - Total number of studies with p<0.025: k = 50 (43.1%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.016 -6.251 0.000 -8.037     0
## Flatness test           0.053  0.347 0.636  9.925     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 36% (23.4%-49.7%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##                Median power for detecting a SESOI of d = .20 
##                                                        0.202 
##                Median power for detecting a SESOI of d = .50 
##                                                        0.801 
##                Median power for detecting a SESOI of d = .70 
##                                                        0.976 
## Median power for detecting PET-PEESE estimate.PEESE estimate 
##                                                        0.209 
##               Median power for detecting 4/3PSM estimate.est 
##                                                        0.286

Comparison of effect types

Model without covariates

## $`Model results`
## $`Model results`$test
##                        Coef. Estimate     SE t-stat p-val (z) Sig.
## 1 factor(effectCompPriming)1    0.211 0.0644   3.27   0.00109   **
## 2 factor(effectCompPriming)2    0.485 0.0321  15.13   < 0.001  ***
## 
## $`Model results`$CIs
##                         Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 factor(effectCompPriming)1    0.211 0.0644  Inf       0.0842        0.337
## 2 factor(effectCompPriming)2    0.485 0.0321  Inf       0.4221        0.548
## 
## 
## $`RVE Wald test`
##  test Fstat df_num df_denom  p_val sig
##   HTZ  15.3      1     35.3 <0.001 ***

Model with covariates

Controlling for design-related factors that are prognostic w.r.t. the effect sizes (i.e., might vary across moderator categories), namely rct, published, sourceTargetDirectionality, and studentSample.

## $`Model results`
## $`Model results`$test
##                                 Coef. Estimate     SE  t-stat p-val (z) Sig.
## 1          factor(effectCompPriming)1  0.00226 0.0900  0.0251    0.9799     
## 2          factor(effectCompPriming)2  0.19823 0.1046  1.8951    0.0581    .
## 3                                 rct  0.14418 0.0747  1.9312    0.0535    .
## 4                           published  0.15282 0.0730  2.0924    0.0364    *
## 5 sourceTargetDirectionality_reconcil -0.03291 0.0806 -0.4083    0.6831     
## 6                       studentSample  0.06213 0.0751  0.8269    0.4083     
## 
## $`Model results`$CIs
##                                  Coef Estimate     SE d.f. Lower 95% CI
## 1          factor(effectCompPriming)1  0.00226 0.0900  Inf     -0.17419
## 2          factor(effectCompPriming)2  0.19823 0.1046  Inf     -0.00679
## 3                                 rct  0.14418 0.0747  Inf     -0.00215
## 4                           published  0.15282 0.0730  Inf      0.00967
## 5 sourceTargetDirectionality_reconcil -0.03291 0.0806  Inf     -0.19087
## 6                       studentSample  0.06213 0.0751  Inf     -0.08514
##   Upper 95% CI
## 1        0.179
## 2        0.403
## 3        0.291
## 4        0.296
## 5        0.125
## 6        0.209
## 
## 
## $`RVE Wald test`
##  test Fstat df_num df_denom   p_val sig
##   HTZ  9.43      1     22.3 0.00554  **
## null device 
##           1

Plots

Contour enhanced funnel plot

Compensatory

Priming

Forest plots

Compensatory

Priming

p-curve plots

Compensatory

Priming

PET-PEESE plots

Using the sqrt(2/n) and 2/n terms instead of SE and var for PET and PEESE, respectively since modified sample-size based estimator was implemented (see https://www.jepusto.com/pet-peese-performance/).

Compensatory

Priming

## null device 
##           1

Mood

Results

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 17; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0000  0.0000     11     no         study 
## sigma^2.2  0.0000  0.0000     17     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 16) = 7.1492, p-val = 0.9703
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.2315  0.0698  3.3145  0.0009  0.0946  0.3684  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.231 0.0448   5.17  8.9       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.231 0.0448  8.9         0.13        0.333
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##     0.131     0.332 
## 
## $Heterogeneity
##                           Tau                           I^2 
##              0.00000295647900              0.00000001354476 
##                 Jackson's I^2 Between-cluster heterogeneity 
##              0.00000000000000              0.00000000000000 
##  Within-cluster heterogeneity                           ICC 
##              0.00000000000000              1.00000000000000 
## 
## $`Proportion of significant results`
## [1] 0.1176471
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Forest plot

Funnel plot

Overall effect results

Results

Number of iterations run equal to 200 for p-curve and 5000 for all other bias correction functions.

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 175; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0438  0.2092    116     no         study 
## sigma^2.2  0.0357  0.1888    175     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 174) = 677.7377, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.4085  0.0315  12.9864  <.0001  0.3469  0.4702  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.409 0.0315     13  105       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.409 0.0315  105        0.346        0.471
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.153     0.970 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.2818135                    74.9082576 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    85.1400000                    41.2700000 
##  Within-cluster heterogeneity                           ICC 
##                    33.6400000                     0.5500000 
## 
## $`Proportion of significant results`
## [1] 0.5923913
## 
## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.5796001
## 
## $`Publication bias`$`4/3PSM`
##     est      se  zvalue  pvalue    ciLB    ciUB       k   steps 
##   0.187   0.052   3.599   0.000   0.085   0.289 114.000   2.000 
## 
## $`Publication bias`$`PET-PEESE`
## PEESE estimate             se         zvalue         pvalue           ciLB 
##          0.159          0.053          3.008          0.003          0.054 
##           ciUB   PET estimate             se         zvalue         pvalue 
##          0.264         -0.138          0.072         -1.915          0.058 
##           ciLB           ciUB 
##         -0.280          0.005 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term  estimate  std.error  statistic   p.value   conf.low conf.high
## 1 WAAP-WLS   b0 0.1493071 0.09129957 0.09129957 0.2436033 -0.2435232 0.5421375
##   type kAdequate
## 1    1         3
## 
## $`Publication bias`$`p-uniform*`
##          est         ciLB         ciUB       pvalue 
## 0.1944982257 0.0957078307 0.2920326952 0.0005655032 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 166 
## - Total number of p<0.05 studies included into the analysis: k = 115 (69.28%) 
## - Total number of studies with p<0.025: k = 75 (45.18%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull   zHalf pHalf
## Right-skewness test     0.001 -8.782 0.000 -10.217     0
## Flatness test           0.087  1.653 0.951  13.089     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 45% (33.4%-55.5%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##                Median power for detecting a SESOI of d = .20 
##                                                        0.215 
##                Median power for detecting a SESOI of d = .50 
##                                                        0.830 
##                Median power for detecting a SESOI of d = .70 
##                                                        0.983 
## Median power for detecting PET-PEESE estimate.PEESE estimate 
##                                                        0.153 
##               Median power for detecting 4/3PSM estimate.est 
##                                                        0.193

Forest plot

Funnel plot

p-curve plot

Methods

Moderator analysis

## $`Model results`
## $`Model results`$test
##                                     Coef. Estimate     SE t-stat p-val (z) Sig.
## 1 methodPhysical temperature manipulation    0.464 0.0629   7.37   < 0.001  ***
## 2   methodVisual/verbal temperature prime    0.485 0.0589   8.23   < 0.001  ***
## 3               methodOutside temperature    0.379 0.1512   2.51   0.01223    *
## 4        methodTemperature estimate as DV    0.477 0.0703   6.79   < 0.001  ***
## 5  methodSubjective warmth judgment as DV    0.299 0.1149   2.61   0.00915   **
## 
## $`Model results`$CIs
##                                      Coef Estimate     SE d.f. Lower 95% CI
## 1 methodPhysical temperature manipulation    0.464 0.0629  Inf       0.3406
## 2   methodVisual/verbal temperature prime    0.485 0.0589  Inf       0.3693
## 3               methodOutside temperature    0.379 0.1512  Inf       0.0825
## 4        methodTemperature estimate as DV    0.477 0.0703  Inf       0.3396
## 5  methodSubjective warmth judgment as DV    0.299 0.1149  Inf       0.0743
##   Upper 95% CI
## 1        0.587
## 2        0.600
## 3        0.675
## 4        0.615
## 5        0.525
## 
## 
## $`RVE Wald test`
##  test Fstat df_num df_denom p_val sig
##   HTZ 0.682      4     17.5 0.614

Results for different methods

Leaving out the Core temperature measurement and Skin temperature measurement, since k is too low

Meta-analysis results

#’ Brief results

## $`Physical temperature manipulation`
##                 k        g [95% CI]                SE               Tau 
##                83 0.48 [0.36, 0.59]              0.06              0.34 
##               I^2 
##               69% 
## 
## $`Visual/verbal temperature prime`
##                 k        g [95% CI]                SE               Tau 
##                23 0.44 [0.35, 0.53]              0.04              0.15 
##               I^2 
##               38% 
## 
## $`Outside temperature`
##                  k         g [95% CI]                 SE                Tau 
##                 13 0.16 [-0.02, 0.34]               0.06                0.1 
##                I^2 
##                44% 
## 
## $`Temperature estimate as DV`
##                 k        g [95% CI]                SE               Tau 
##                25 0.39 [0.27, 0.52]              0.06              0.19 
##               I^2 
##               60% 
## 
## $`Subjective warmth judgment as DV`
##                 k        g [95% CI]                SE               Tau 
##                14 0.28 [0.03, 0.53]              0.11              0.37 
##               I^2 
##               93%

Physical temperature manipulation

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 82; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.1003  0.3167     51     no         study 
## sigma^2.2  0.0153  0.1238     82     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 81) = 339.5264, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.4779  0.0575  8.3075  <.0001  0.3652  0.5907  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.478 0.0576    8.3 48.2       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.478 0.0576 48.2        0.362        0.594
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.215     1.171 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.3400426                    69.0555528 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    80.9300000                    59.9000000 
##  Within-cluster heterogeneity                           ICC 
##                     9.1500000                     0.8700000 
## 
## $`Proportion of significant results`
## [1] 0.6746988
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Visual/verbal temperature prime

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 23; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0000  0.0000     14     no         study 
## sigma^2.2  0.0232  0.1524     23     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 22) = 38.5260, p-val = 0.0160
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.4398  0.0600  7.3355  <.0001  0.3223  0.5573  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt     0.44 0.0411   10.7 11.4       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt     0.44 0.0411 11.4         0.35         0.53
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##     0.099     0.781 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.1523991                    37.8013636 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    13.6800000                     0.0000000 
##  Within-cluster heterogeneity                           ICC 
##                    37.8000000                     0.0000000 
## 
## $`Proportion of significant results`
## [1] 0.6956522
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Outside temperature

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 8; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0077  0.0875      6     no         study 
## sigma^2.2  0.0028  0.0525      8     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 7) = 14.2050, p-val = 0.0477
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.1607  0.0642  2.5034  0.0123  0.0349  0.2864  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.161 0.0634   2.54 3.63       0.0705    .
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.161 0.0634 3.63      -0.0225        0.344
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.147     0.468 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.1020439                    44.1844740 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    56.9100000                    32.5100000 
##  Within-cluster heterogeneity                           ICC 
##                    11.6700000                     0.7400000 
## 
## $`Proportion of significant results`
## [1] 0.1538462
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Temperature estimate as DV

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 23; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0378  0.1945     21     no         study 
## sigma^2.2  0.0000  0.0000     23     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 22) = 49.5679, p-val = 0.0007
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.3931  0.0599  6.5631  <.0001  0.2757  0.5104  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.393 0.0601   6.54   17       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.393 0.0601   17        0.266         0.52
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.031     0.817 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.1945094                    59.9035594 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    71.0500000                    59.9000000 
##  Within-cluster heterogeneity                           ICC 
##                     0.0000000                     1.0000000 
## 
## $`Proportion of significant results`
## [1] 0.72
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Subjective warmth judgment as DV

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 13; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.1253  0.3539     12     no         study 
## sigma^2.2  0.0089  0.0942     13     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 12) = 69.2488, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.2810  0.1153  2.4362  0.0148  0.0549  0.5070  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate    SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.281 0.115   2.45 10.7        0.033    *
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate    SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.281 0.115 10.7       0.0274        0.535
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.564     1.126 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.3662546                    92.6858591 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    95.7600000                    86.5600000 
##  Within-cluster heterogeneity                           ICC 
##                     6.1300000                     0.9300000 
## 
## $`Proportion of significant results`
## [1] 0.5714286
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Bias correction results

Physical temperature manipulation

## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.5226101
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.151  0.094  1.603  0.109 -0.034  0.336 51.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
##   PET estimate             se         zvalue         pvalue           ciLB 
##         -0.254          0.225         -1.128          0.265         -0.705 
##           ciUB PEESE estimate             se         zvalue         pvalue 
##          0.198          0.190          0.133          1.424          0.161 
##           ciLB           ciUB 
##         -0.078          0.457 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term   estimate std.error statistic   p.value  conf.low conf.high
## 1 WAAP-WLS   b0 -0.4364618  0.251926  0.251926 0.2253231 -1.520412 0.6474885
##   type kAdequate
## 1    1         3
## 
## $`Publication bias`$`p-uniform*`
##         est        ciLB        ciUB      pvalue 
##  0.06957033 -0.13193200  0.27026087  0.52185334 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 82 
## - Total number of p<0.05 studies included into the analysis: k = 58 (70.73%) 
## - Total number of studies with p<0.025: k = 33 (40.24%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.179 -6.720 0.000 -8.942     0
## Flatness test           0.013  1.791 0.963 10.255     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 50% (34.7%-65%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##              Median power for detecting a SESOI of d = .20 
##                                                     "0.19" 
##              Median power for detecting a SESOI of d = .50 
##                                                    "0.768" 
##              Median power for detecting a SESOI of d = .70 
##                                                    "0.965" 
## Median power for detecting PET-PEESE estimate.PET estimate 
##                    "ES estimate in the opposite direction" 
##             Median power for detecting 4/3PSM estimate.est 
##                                                    "0.129"

Visual/verbal temperature prime

## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.3287924
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.271  0.080  3.400  0.001  0.115  0.428 14.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
## PEESE estimate             se         zvalue         pvalue           ciLB 
##          0.292          0.110          2.662          0.021          0.053 
##           ciUB   PET estimate             se         zvalue         pvalue 
##          0.532          0.182          0.207          0.878          0.397 
##           ciLB           ciUB 
##         -0.270          0.634 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term  estimate  std.error  statistic     p.value  conf.low conf.high
## 1 WAAP-WLS   b0 0.3037822 0.03003057 0.03003057 0.009631472 0.1745711 0.4329934
##   type kAdequate
## 1    1         3
## 
## $`Publication bias`$`p-uniform*`
##        est       ciLB       ciUB     pvalue 
## 0.31186560 0.09457384 0.49685638 0.23877258 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 23 
## - Total number of p<0.05 studies included into the analysis: k = 19 (82.61%) 
## - Total number of studies with p<0.025: k = 14 (60.87%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.032 -4.527 0.000 -4.793     0
## Flatness test           0.670  1.450 0.926  5.666     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 56% (30.3%-77.3%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##                Median power for detecting a SESOI of d = .20 
##                                                        0.202 
##                Median power for detecting a SESOI of d = .50 
##                                                        0.801 
##                Median power for detecting a SESOI of d = .70 
##                                                        0.976 
## Median power for detecting PET-PEESE estimate.PEESE estimate 
##                                                        0.374 
##               Median power for detecting 4/3PSM estimate.est 
##                                                        0.331

Temperature estimate as DV

## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.7500059
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.046  0.058  0.779  0.436 -0.069  0.160 21.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
##   PET estimate             se         zvalue         pvalue           ciLB 
##         -0.086          0.091         -0.942          0.358         -0.277 
##           ciUB PEESE estimate             se         zvalue         pvalue 
##          0.105          0.108          0.062          1.742          0.098 
##           ciLB           ciUB 
##         -0.022          0.238 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term   estimate std.error statistic   p.value  conf.low conf.high
## 1 WAAP-WLS   b0 0.04964282 0.1242567 0.1242567 0.7580266 -1.529189  1.628474
##   type kAdequate
## 1    1         2
## 
## $`Publication bias`$`p-uniform*`
##        est       ciLB       ciUB     pvalue 
##  0.0556670 -0.1507642  0.2412312  0.6814799 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 24 
## - Total number of p<0.05 studies included into the analysis: k = 18 (75%) 
## - Total number of studies with p<0.025: k = 10 (41.67%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.407 -0.951 0.171 -1.939 0.026
## Flatness test           0.112 -1.590 0.056  3.610 1.000
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 10% (5%-34.4%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##              Median power for detecting a SESOI of d = .20 
##                                                    "0.202" 
##              Median power for detecting a SESOI of d = .50 
##                                                    "0.801" 
##              Median power for detecting a SESOI of d = .70 
##                                                    "0.976" 
## Median power for detecting PET-PEESE estimate.PET estimate 
##                    "ES estimate in the opposite direction" 
##             Median power for detecting 4/3PSM estimate.est 
##                                                    "0.058"

Funnel plots

Forest plots

Categories

Leaving out the Robotics and Neural Mechanisms, since k is too low

Results for different categories

Meta-analysis results

Brief results

## $Emotion
##                 k        g [95% CI]                SE               Tau 
##                24 0.39 [0.31, 0.46]              0.03              0.15 
##               I^2 
##               54% 
## 
## $Interpersonal
##                 k        g [95% CI]                SE               Tau 
##                76 0.36 [0.26, 0.46]              0.05              0.31 
##               I^2 
##               78% 
## 
## $`Person perception`
##                 k        g [95% CI]                SE               Tau 
##                39 0.41 [0.23, 0.58]              0.08              0.33 
##               I^2 
##               81% 
## 
## $`Group processes`
##                 k        g [95% CI]                SE               Tau 
##                12 0.62 [0.38, 0.85]              0.09                 0 
##               I^2 
##                0% 
## 
## $`Moral judgment`
##                 k        g [95% CI]                SE               Tau 
##                 6 0.49 [-0.12, 1.1]              0.11              0.03 
##               I^2 
##                2% 
## 
## $`Self-regulation`
##                 k        g [95% CI]                SE               Tau 
##                26 0.32 [0.17, 0.47]              0.07              0.27 
##               I^2 
##               76% 
## 
## $`Cognitive processes`
##                 k        g [95% CI]                SE               Tau 
##                36 0.56 [0.46, 0.66]              0.05              0.12 
##               I^2 
##               20% 
## 
## $`Economic decision-making`
##                 k        g [95% CI]                SE               Tau 
##                43 0.44 [0.28, 0.59]              0.07              0.31 
##               I^2 
##               70%

Emotion

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 23; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0000  0.0000     19     no         study 
## sigma^2.2  0.0233  0.1526     23     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 22) = 44.6519, p-val = 0.0029
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.3859  0.0506  7.6308  <.0001  0.2868  0.4850  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.386 0.0343   11.2 15.3       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.386 0.0343 15.3        0.313        0.459
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##     0.057     0.715 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.1526369                    54.0804386 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    65.7500000                     0.0000000 
##  Within-cluster heterogeneity                           ICC 
##                    54.0800000                     0.0000000 
## 
## $`Proportion of significant results`
## [1] 0.7083333
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Interpersonal

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 75; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0503  0.2242     56     no         study 
## sigma^2.2  0.0488  0.2208     75     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 74) = 364.3079, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.3594  0.0498  7.2141  <.0001  0.2617  0.4570  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.359 0.0499   7.21 51.7       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.359 0.0499 51.7        0.259        0.459
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.279     0.998 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.3146483                    77.6948925 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    85.2200000                    39.4400000 
##  Within-cluster heterogeneity                           ICC 
##                    38.2600000                     0.5100000 
## 
## $`Proportion of significant results`
## [1] 0.5789474
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Person perception

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 36; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0765  0.2766     21     no         study 
## sigma^2.2  0.0337  0.1835     36     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 35) = 133.8868, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.4075  0.0829  4.9175  <.0001  0.2451  0.5698  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.407 0.0828   4.92 18.3       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.407 0.0828 18.3        0.234        0.581
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.306     1.121 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.3319022                    81.0462124 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    90.0500000                    56.2800000 
##  Within-cluster heterogeneity                           ICC 
##                    24.7700000                     0.6900000 
## 
## $`Proportion of significant results`
## [1] 0.4358974
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Group processes

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 11; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0000  0.0000      7     no         study 
## sigma^2.2  0.0000  0.0000     11     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 10) = 8.8815, p-val = 0.5434
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.6152  0.1079  5.7024  <.0001  0.4037  0.8266  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate    SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.615 0.094   6.55  5.3       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate    SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.615 0.094  5.3        0.378        0.853
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##     0.386     0.845 
## 
## $Heterogeneity
##                           Tau                           I^2 
##              0.00000462993551              0.00000002010181 
##                 Jackson's I^2 Between-cluster heterogeneity 
##              0.00000000000000              0.00000000000000 
##  Within-cluster heterogeneity                           ICC 
##              0.00000000000000              0.83000000000000 
## 
## $`Proportion of significant results`
## [1] 0.8333333
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Moral judgment

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0000  0.0000      3     no         study 
## sigma^2.2  0.0011  0.0329      6     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 5) = 4.6596, p-val = 0.4588
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.4878  0.1076  4.5335  <.0001  0.2769  0.6987  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate    SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.488 0.111   4.41 1.61       0.0701    .
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate    SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.488 0.111 1.61        -0.12          1.1
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##     0.027     0.948 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                    0.03292223                    2.34692161 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                   25.18000000                    0.00000000 
##  Within-cluster heterogeneity                           ICC 
##                    2.35000000                    0.00000000 
## 
## $`Proportion of significant results`
## [1] NA
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Self-regulation

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 24; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0312  0.1765     21     no         study 
## sigma^2.2  0.0436  0.2089     24     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 23) = 90.2528, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.3242  0.0716  4.5298  <.0001  0.1839  0.4645  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.324 0.0714   4.54 18.7       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.324 0.0714 18.7        0.175        0.474
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.265     0.914 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.2734749                    76.4112635 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    84.2000000                    31.8400000 
##  Within-cluster heterogeneity                           ICC 
##                    44.5800000                     0.4200000 
## 
## $`Proportion of significant results`
## [1] 0.5384615
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Cognitive processes

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 35; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0000  0.0000     29     no         study 
## sigma^2.2  0.0143  0.1194     35     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 34) = 40.8129, p-val = 0.1959
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.5566  0.0485  11.4785  <.0001  0.4616  0.6517  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.557 0.0478   11.6 23.2       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.557 0.0478 23.2        0.458        0.655
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##     0.293     0.820 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.1194099                    19.7930556 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    26.6100000                     0.0000000 
##  Within-cluster heterogeneity                           ICC 
##                    19.7900000                     0.0000000 
## 
## $`Proportion of significant results`
## [1] 0.75
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"

Economic decision-making

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 38; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0671  0.2590     25     no         study 
## sigma^2.2  0.0306  0.1750     38     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 37) = 117.7860, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.4361  0.0743  5.8697  <.0001  0.2905  0.5818  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.436 0.0743   5.87 22.7       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.436 0.0743 22.7        0.282         0.59
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.227     1.099 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                      0.312574                     70.182750 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                     81.650000                     48.180000 
##  Within-cluster heterogeneity                           ICC 
##                     22.000000                      0.690000 
## 
## $`Proportion of significant results`
## [1] 0.5581395
## 
## $`Publication bias`
## [1] "Publication bias corrections not carried out"
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
## [1] "Power for detecting bias-corrected parameter estimates not computed"
##      3PSM est [95% CI]            3PSM.pvalue PET-PEESE est [95% CI] 
##     0.23 [-0.01, 0.48]                  0.061     0.24 [-0.04, 0.52] 
##       PET-PEESE.pvalue 
##                  0.086
## $Emotion
##                 k        g [95% CI]                SE               Tau 
##                24 0.39 [0.31, 0.46]              0.03              0.15 
##               I^2 
##               54% 
## 
## $Interpersonal
##                 k        g [95% CI]                SE               Tau 
##                76 0.36 [0.26, 0.46]              0.05              0.31 
##               I^2 
##               78% 
## 
## $`Person perception`
##                 k        g [95% CI]                SE               Tau 
##                39 0.41 [0.23, 0.58]              0.08              0.33 
##               I^2 
##               81% 
## 
## $`Group processes`
##                 k        g [95% CI]                SE               Tau 
##                12 0.62 [0.38, 0.85]              0.09                 0 
##               I^2 
##                0% 
## 
## $`Moral judgment`
##                 k        g [95% CI]                SE               Tau 
##                 6 0.49 [-0.12, 1.1]              0.11              0.03 
##               I^2 
##                2% 
## 
## $`Self-regulation`
##                 k        g [95% CI]                SE               Tau 
##                26 0.32 [0.17, 0.47]              0.07              0.27 
##               I^2 
##               76% 
## 
## $`Cognitive processes`
##                 k        g [95% CI]                SE               Tau 
##                36 0.56 [0.46, 0.66]              0.05              0.12 
##               I^2 
##               20% 
## 
## $`Economic decision-making`
##                 k        g [95% CI]                SE               Tau 
##                43 0.44 [0.28, 0.59]              0.07              0.31 
##               I^2 
##               70%

Bias correction results

Emotion

## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.2669407
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.260  0.028  9.171  0.000  0.204  0.315 19.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
## PEESE estimate             se         zvalue         pvalue           ciLB 
##          0.353          0.050          7.110          0.000          0.249 
##           ciUB   PET estimate             se         zvalue         pvalue 
##          0.458          0.301          0.064          4.724          0.000 
##           ciLB           ciUB 
##          0.167          0.436 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term  estimate  std.error  statistic           p.value  conf.low
## 1 WAAP-WLS   b0 0.3105771 0.02925429 0.02925429 0.000000000402088 0.2499074
##   conf.high type kAdequate
## 1 0.3712468    2         1
## 
## $`Publication bias`$`p-uniform*`
##        est       ciLB       ciUB     pvalue 
## 0.30896207 0.19689270 0.41491368 0.01497127 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 31 
## - Total number of p<0.05 studies included into the analysis: k = 23 (74.19%) 
## - Total number of studies with p<0.025: k = 14 (45.16%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.202 -4.994 0.000 -6.551     0
## Flatness test           0.185  1.740 0.959  7.135     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 59% (34.7%-78%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##                Median power for detecting a SESOI of d = .20 
##                                                        0.331 
##                Median power for detecting a SESOI of d = .50 
##                                                        0.968 
##                Median power for detecting a SESOI of d = .70 
##                                                        1.000 
## Median power for detecting PET-PEESE estimate.PEESE estimate 
##                                                        0.767 
##               Median power for detecting 4/3PSM estimate.est 
##                                                        0.508

Interpersonal

## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.5905207
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.100  0.078  1.295  0.195 -0.052  0.252 56.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
##   PET estimate             se         zvalue         pvalue           ciLB 
##         -0.215          0.107         -2.000          0.051         -0.430 
##           ciUB PEESE estimate             se         zvalue         pvalue 
##          0.000          0.098          0.077          1.276          0.207 
##           ciLB           ciUB 
##         -0.056          0.252 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term  estimate  std.error  statistic     p.value   conf.low
## 1 WAAP-WLS   b0 0.1388678 0.04068096 0.04068096 0.001043176 0.05780919
##   conf.high type kAdequate
## 1 0.2199264    2         1
## 
## $`Publication bias`$`p-uniform*`
##         est        ciLB        ciUB      pvalue 
##  0.07458773 -0.08100454  0.23262782  0.35226835 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 87 
## - Total number of p<0.05 studies included into the analysis: k = 56 (64.37%) 
## - Total number of studies with p<0.025: k = 31 (35.63%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.252 -4.513 0.000 -6.905     0
## Flatness test           0.008 -0.084 0.466  8.829     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 32% (17.9%-49.2%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##              Median power for detecting a SESOI of d = .20 
##                                                    "0.205" 
##              Median power for detecting a SESOI of d = .50 
##                                                    "0.808" 
##              Median power for detecting a SESOI of d = .70 
##                                                    "0.977" 
## Median power for detecting PET-PEESE estimate.PET estimate 
##                    "ES estimate in the opposite direction" 
##             Median power for detecting 4/3PSM estimate.est 
##                                                    "0.087"

Person perception

## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.6107588
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.442  0.125  3.528  0.000  0.196  0.687 21.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
## PEESE estimate             se         zvalue         pvalue           ciLB 
##          0.060          0.050          1.181          0.252         -0.046 
##           ciUB   PET estimate             se         zvalue         pvalue 
##          0.165         -0.236          0.064         -3.710          0.001 
##           ciLB           ciUB 
##         -0.369         -0.103 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term  estimate  std.error  statistic      p.value   conf.low
## 1 WAAP-WLS   b0 0.1963766 0.05084897 0.05084897 0.0004645318 0.09314768
##   conf.high type kAdequate
## 1 0.2996055    2         0
## 
## $`Publication bias`$`p-uniform*`
##       est      ciLB      ciUB    pvalue 
## 0.3497897 0.1311900 0.5620256 0.0055566 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 27 
## - Total number of p<0.05 studies included into the analysis: k = 18 (66.67%) 
## - Total number of studies with p<0.025: k = 12 (44.44%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.119 -5.415 0.000 -6.263     0
## Flatness test           0.413  2.363 0.991  6.659     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 69% (44.9%-85.8%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##                Median power for detecting a SESOI of d = .20 
##                                                        0.253 
##                Median power for detecting a SESOI of d = .50 
##                                                        0.899 
##                Median power for detecting a SESOI of d = .70 
##                                                        0.995 
## Median power for detecting PET-PEESE estimate.PEESE estimate 
##                                                        0.067 
##               Median power for detecting 4/3PSM estimate.est 
##                                                        0.816

Self-regulation

## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.6350534
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.068  0.114  0.600  0.548 -0.155  0.292 21.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
##   PET estimate             se         zvalue         pvalue           ciLB 
##         -0.318          0.101         -3.148          0.005         -0.530 
##           ciUB PEESE estimate             se         zvalue         pvalue 
##         -0.107         -0.003          0.056         -0.057          0.955 
##           ciLB           ciUB 
##         -0.120          0.114 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term estimate  std.error  statistic    p.value   conf.low conf.high
## 1 WAAP-WLS   b0   0.1496 0.05771728 0.05771728 0.01630102 0.03020272 0.2689973
##   type kAdequate
## 1    2         1
## 
## $`Publication bias`$`p-uniform*`
##         est        ciLB        ciUB      pvalue 
##  0.12567927 -0.05613884  0.31311091  0.18274946 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 35 
## - Total number of p<0.05 studies included into the analysis: k = 24 (68.57%) 
## - Total number of studies with p<0.025: k = 15 (42.86%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.154 -2.474 0.007 -2.071 0.019
## Flatness test           0.225 -0.642 0.260  4.357 1.000
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 24% (8.1%-48.8%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##              Median power for detecting a SESOI of d = .20 
##                                                    "0.396" 
##              Median power for detecting a SESOI of d = .50 
##                                                    "0.989" 
##              Median power for detecting a SESOI of d = .70 
##                                                        "1" 
## Median power for detecting PET-PEESE estimate.PET estimate 
##                    "ES estimate in the opposite direction" 
##             Median power for detecting 4/3PSM estimate.est 
##                                                    "0.089"

Cognitive processes

## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.3800724
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.344  0.129  2.678  0.007  0.092  0.596 28.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
## PEESE estimate             se         zvalue         pvalue           ciLB 
##          0.405          0.083          4.859          0.000          0.234 
##           ciUB   PET estimate             se         zvalue         pvalue 
##          0.576          0.218          0.146          1.490          0.148 
##           ciLB           ciUB 
##         -0.082          0.519 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term  estimate std.error statistic    p.value  conf.low conf.high
## 1 WAAP-WLS   b0 0.4210579 0.1142019 0.1142019 0.02107484 0.1039825 0.7381332
##   type kAdequate
## 1    1         5
## 
## $`Publication bias`$`p-uniform*`
##        est       ciLB       ciUB     pvalue 
## 0.24115193 0.01326872 0.44907076 0.21851476 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 51 
## - Total number of p<0.05 studies included into the analysis: k = 38 (74.51%) 
## - Total number of studies with p<0.025: k = 25 (49.02%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.036 -5.536  0.00 -6.390     0
## Flatness test           0.273  1.403  0.92  7.798     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 50% (30.6%-67.2%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##                Median power for detecting a SESOI of d = .20 
##                                                        0.197 
##                Median power for detecting a SESOI of d = .50 
##                                                        0.789 
##                Median power for detecting a SESOI of d = .70 
##                                                        0.972 
## Median power for detecting PET-PEESE estimate.PEESE estimate 
##                                                        0.609 
##               Median power for detecting 4/3PSM estimate.est 
##                                                        0.476

Economic decision-making

## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.6508614
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.234  0.125  1.875  0.061 -0.011  0.478 24.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
## PEESE estimate             se         zvalue         pvalue           ciLB 
##          0.241          0.134          1.792          0.086         -0.037 
##           ciUB   PET estimate             se         zvalue         pvalue 
##          0.519         -0.135          0.257         -0.526          0.604 
##           ciLB           ciUB 
##         -0.668          0.397 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term  estimate  std.error  statistic       p.value  conf.low
## 1 WAAP-WLS   b0 0.3017689 0.05936583 0.05936583 0.00001090482 0.1814823
##   conf.high type kAdequate
## 1 0.4220555    2         0
## 
## $`Publication bias`$`p-uniform*`
##          est         ciLB         ciUB       pvalue 
##  0.229290001 -0.009305462  0.461452354  0.089691375 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 36 
## - Total number of p<0.05 studies included into the analysis: k = 27 (75%) 
## - Total number of studies with p<0.025: k = 14 (38.89%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.500 -2.332 0.010 -3.352     0
## Flatness test           0.024 -0.937 0.174  4.609     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 21% (7.3%-43.7%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##                Median power for detecting a SESOI of d = .20 
##                                                        0.202 
##                Median power for detecting a SESOI of d = .50 
##                                                        0.801 
##                Median power for detecting a SESOI of d = .70 
##                                                        0.976 
## Median power for detecting PET-PEESE estimate.PEESE estimate 
##                                                        0.272 
##               Median power for detecting 4/3PSM estimate.est 
##                                                        0.260

Contour enhanced funnel plots

Forest plots

## null device 
##           1

Moderator/sensitivity analyses

The below reported meta-regressions are all implemented as a multivariate RVE-based models using the CHE working model (Pustejovsky & Tipton, 2020; https://osf.io/preprints/metaarxiv/vyfcj/). Testing of contrasts is carried out using a robust Wald-type test testing the equality of estimates across levels of the moderator.

Moderation by citations and IF

Overall effect moderated by citations and IF

## $test
##                              Coef. Estimate     SE t-stat p-val (z) Sig.
## 1                          intrcpt   0.4209 0.0307  13.70    <0.001  ***
## 2           scale(publicationYear)  -0.0334 0.0293  -1.14    0.2535     
## 3      scale(citationsGSMarch2016)   0.0626 0.0309   2.03    0.0424    *
## 4 scale(h5indexGSJournalMarch2016)  -0.0880 0.0403  -2.18    0.0290    *
## 
## $CIs
##                               Coef Estimate     SE d.f. Lower 95% CI
## 1                          intrcpt   0.4209 0.0307  Inf      0.36071
## 2           scale(publicationYear)  -0.0334 0.0293  Inf     -0.09081
## 3      scale(citationsGSMarch2016)   0.0626 0.0309  Inf      0.00214
## 4 scale(h5indexGSJournalMarch2016)  -0.0880 0.0403  Inf     -0.16688
##   Upper 95% CI
## 1      0.48116
## 2      0.02395
## 3      0.12315
## 4     -0.00903

Moderation by lattitude

Overall effect moderated by lattitude

## $test
##                       Coef. Estimate     SE t-stat p-val (z) Sig.
## 1                   intrcpt   0.4584 0.0367 12.482    <0.001  ***
## 2 scale(latitudeUniversity)   0.0267 0.0362  0.737     0.461     
## 
## $CIs
##                        Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1                   intrcpt   0.4584 0.0367  Inf       0.3864       0.5304
## 2 scale(latitudeUniversity)   0.0267 0.0362  Inf      -0.0442       0.0975

Compensatory effects moderated by lattitude

## $test
##                       Coef. Estimate     SE t-stat p-val (z) Sig.
## 1                   intrcpt   0.2753 0.0716  3.844    <0.001  ***
## 2 scale(latitudeUniversity)  -0.0355 0.0625 -0.567      0.57     
## 
## $CIs
##                        Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1                   intrcpt   0.2753 0.0716  Inf        0.135       0.4157
## 2 scale(latitudeUniversity)  -0.0355 0.0625  Inf       -0.158       0.0871

Priming effects moderated by lattitude

## $test
##                       Coef. Estimate     SE t-stat p-val (z) Sig.
## 1                   intrcpt   0.5076 0.0403  12.60    <0.001  ***
## 2 scale(latitudeUniversity)   0.0589 0.0406   1.45     0.147     
## 
## $CIs
##                        Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1                   intrcpt   0.5076 0.0403  Inf       0.4286        0.587
## 2 scale(latitudeUniversity)   0.0589 0.0406  Inf      -0.0207        0.139

Mood effects moderated by lattitude

## $test
##                       Coef. Estimate     SE t-stat p-val (z) Sig.
## 1                   intrcpt    0.232 0.0463  5.017    <0.001  ***
## 2 scale(latitudeUniversity)   -0.016 0.0353 -0.451     0.652     
## 
## $CIs
##                        Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1                   intrcpt    0.232 0.0463  Inf       0.1417       0.3233
## 2 scale(latitudeUniversity)   -0.016 0.0353  Inf      -0.0852       0.0533

Moderation by gender

Overall effect moderated by gender ratio

## $test
##               Coef. Estimate     SE t-stat p-val (z) Sig.
## 1           intrcpt    0.427 0.0338  12.63   < 0.001  ***
## 2 scale(percFemale)    0.103 0.0388   2.64   0.00821   **
## 
## $CIs
##                Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1           intrcpt    0.427 0.0338  Inf       0.3609        0.494
## 2 scale(percFemale)    0.103 0.0388  Inf       0.0266        0.179

Compensatory effects moderated by gender ratio

## $test
##               Coef. Estimate     SE t-stat p-val (z) Sig.
## 1           intrcpt   0.2887 0.0615  4.694    <0.001  ***
## 2 scale(percFemale)   0.0305 0.0675  0.452     0.651     
## 
## $CIs
##                Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1           intrcpt   0.2887 0.0615  Inf        0.168        0.409
## 2 scale(percFemale)   0.0305 0.0675  Inf       -0.102        0.163

Priming effects moderated by gender ratio

## $test
##               Coef. Estimate     SE t-stat p-val (z) Sig.
## 1           intrcpt    0.466 0.0364  12.80    <0.001  ***
## 2 scale(percFemale)    0.144 0.0428   3.35    <0.001  ***
## 
## $CIs
##                Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1           intrcpt    0.466 0.0364  Inf       0.3944        0.537
## 2 scale(percFemale)    0.144 0.0428  Inf       0.0597        0.227

Published status

## $`Model results`
## $`Model results`$test
##                Coef. Estimate     SE t-stat p-val (z) Sig.
## 1 factor(published)0    0.312 0.0773   4.03    <0.001  ***
## 2 factor(published)1    0.432 0.0347  12.47    <0.001  ***
## 
## $`Model results`$CIs
##                 Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 factor(published)0    0.312 0.0773  Inf        0.160        0.463
## 2 factor(published)1    0.432 0.0347  Inf        0.364        0.500
## 
## 
## $`RVE Wald test`
##  test Fstat df_num df_denom p_val sig
##   HTZ  2.05      1     24.3 0.164

Comparing randomized and non-randomized designs

## $`Model results`
## $`Model results`$test
##          Coef. Estimate     SE t-stat p-val (z) Sig.
## 1 factor(rct)0    0.260 0.0543   4.79    <0.001  ***
## 2 factor(rct)1    0.447 0.0360  12.40    <0.001  ***
## 
## $`Model results`$CIs
##           Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 factor(rct)0    0.260 0.0543  Inf        0.154        0.367
## 2 factor(rct)1    0.447 0.0360  Inf        0.376        0.517
## 
## 
## $`RVE Wald test`
##  test Fstat df_num df_denom   p_val sig
##   HTZ  8.72      1     34.7 0.00562  **

Subgroups of observational and randomized effects

Non-randomized

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 29; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0387  0.1968     24     no         study 
## sigma^2.2  0.0116  0.1078     29     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 28) = 102.6511, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.2554  0.0567  4.5059  <.0001  0.1443  0.3665  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.255 0.0564   4.52 21.1       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.255 0.0564 21.1        0.138        0.373
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.223     0.734 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.2243704                    79.6290061 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    89.8100000                    61.2600000 
##  Within-cluster heterogeneity                           ICC 
##                    18.3700000                     0.7700000 
## 
## $`Proportion of significant results`
## [1] 0.3823529
## 
## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.4714771
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.092  0.059  1.569  0.117 -0.023  0.208 24.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
##   PET estimate             se         zvalue         pvalue           ciLB 
##         -0.097          0.104         -0.938          0.358         -0.312 
##           ciUB PEESE estimate             se         zvalue         pvalue 
##          0.118          0.094          0.061          1.536          0.139 
##           ciLB           ciUB 
##         -0.033          0.222 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term  estimate  std.error  statistic   p.value   conf.low conf.high
## 1 WAAP-WLS   b0 0.1493071 0.09129957 0.09129957 0.2436033 -0.2435232 0.5421375
##   type kAdequate
## 1    1         3
## 
## $`Publication bias`$`p-uniform*`
##        est       ciLB       ciUB     pvalue 
## 0.14359084 0.01474893 0.27467682 0.03523204 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 23 
## - Total number of p<0.05 studies included into the analysis: k = 11 (47.83%) 
## - Total number of studies with p<0.025: k = 7 (30.43%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.274 -3.271 0.001 -3.501     0
## Flatness test           0.389  1.111 0.867  4.880     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 58% (22.5%-83.7%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##              Median power for detecting a SESOI of d = .20 
##                                                    "0.367" 
##              Median power for detecting a SESOI of d = .50 
##                                                    "0.982" 
##              Median power for detecting a SESOI of d = .70 
##                                                        "1" 
## Median power for detecting PET-PEESE estimate.PET estimate 
##                    "ES estimate in the opposite direction" 
##             Median power for detecting 4/3PSM estimate.est 
##                                                    "0.116"

Randomized

## $`RMA results with model-based SEs`
## 
## Multivariate Meta-Analysis Model (k = 144; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed        factor 
## sigma^2.1  0.0464  0.2155     92     no         study 
## sigma^2.2  0.0364  0.1907    144     no  study/result 
## 
## Test for Heterogeneity:
## Q(df = 143) = 559.4052, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.4521  0.0364  12.4333  <.0001  0.3809  0.5234  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`RVE SEs with Satterthwaite small-sample correction`
## $`RVE SEs with Satterthwaite small-sample correction`$test
##     Coef. Estimate     SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt    0.452 0.0364   12.4 82.9       <0.001  ***
## 
## $`RVE SEs with Satterthwaite small-sample correction`$CIs
##      Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 intrcpt    0.452 0.0364 82.9         0.38        0.525
## 
## 
## $`Prediction interval`
## 95% PI LB 95% PI UB 
##    -0.124     1.028 
## 
## $Heterogeneity
##                           Tau                           I^2 
##                     0.2877887                    69.8305082 
##                 Jackson's I^2 Between-cluster heterogeneity 
##                    80.4600000                    39.1500000 
##  Within-cluster heterogeneity                           ICC 
##                    30.6800000                     0.5600000 
## 
## $`Proportion of significant results`
## [1] 0.6351351
## 
## $`Publication bias`
## $`Publication bias`$`ES-precision correlation`
## [1] 0.5386684
## 
## $`Publication bias`$`4/3PSM`
##    est     se zvalue pvalue   ciLB   ciUB      k  steps 
##  0.220  0.064  3.443  0.001  0.095  0.346 92.000  2.000 
## 
## $`Publication bias`$`PET-PEESE`
## PEESE estimate             se         zvalue         pvalue           ciLB 
##          0.186          0.072          2.586          0.011          0.043 
##           ciUB   PET estimate             se         zvalue         pvalue 
##          0.329         -0.145          0.096         -1.511          0.134 
##           ciLB           ciUB 
##         -0.336          0.046 
## 
## $`Publication bias`$`WAAP-WLS`
##     method term    estimate std.error statistic   p.value   conf.low conf.high
## 1 WAAP-WLS   b0 -0.02180643 0.1306439 0.1306439 0.8729201 -0.3414805 0.2978676
##   type kAdequate
## 1    1         7
## 
## $`Publication bias`$`p-uniform*`
##         est        ciLB        ciUB      pvalue 
## 0.205756568 0.082334253 0.325994919 0.006398717 
## 
## $`Publication bias`$`p-curve`
## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 140 
## - Total number of p<0.05 studies included into the analysis: k = 98 (70%) 
## - Total number of studies with p<0.025: k = 63 (45%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.003 -8.542  0.00 -9.642     0
## Flatness test           0.076  1.887  0.97 12.097     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 47% (35.1%-58.7%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no 
## 
## 
## $`Power for detecting SESOI and bias-corrected parameter estimates`
##                Median power for detecting a SESOI of d = .20 
##                                                        0.204 
##                Median power for detecting a SESOI of d = .50 
##                                                        0.806 
##                Median power for detecting a SESOI of d = .70 
##                                                        0.977 
## Median power for detecting PET-PEESE estimate.PEESE estimate 
##                                                        0.183 
##               Median power for detecting 4/3PSM estimate.est 
##                                                        0.237

F-test of equality of variances

Mean vi for non-randomized designs

Mean vi for randomized designs

F-test

## [1] 0.1272321

Comparing effects based on student and non-student samples

## $`Model results`
## $`Model results`$test
##                    Coef. Estimate     SE t-stat p-val (z) Sig.
## 1 factor(studentSample)0    0.327 0.0456   7.16    <0.001  ***
## 2 factor(studentSample)1    0.453 0.0395  11.46    <0.001  ***
## 
## $`Model results`$CIs
##                     Coef Estimate     SE d.f. Lower 95% CI Upper 95% CI
## 1 factor(studentSample)0    0.327 0.0456  Inf        0.237        0.416
## 2 factor(studentSample)1    0.453 0.0395  Inf        0.375        0.530
## 
## 
## $`RVE Wald test`
##  test Fstat df_num df_denom  p_val sig
##   HTZ  4.36      1       81 0.0398   *

Year of Publication

Linear mixed-effects model. Taking into effect clustering of ESs due to originating from the same study. Using square root of variance to make the distribution normal.

##                                    Estimate Std. Error       df   t value
## (Intercept)                      -0.1407496 0.09437155 85.97906 -1.491442
## scale(h5indexGSJournalMarch2016) -0.1697075 0.10151050 85.90369 -1.671823
## scale(publicationYear)           -0.2307599 0.08925779 85.99053 -2.585320
##                                    Pr(>|t|)
## (Intercept)                      0.13950560
## scale(h5indexGSJournalMarch2016) 0.09819827
## scale(publicationYear)           0.01141363

Comment: all the variables were centered for easier interpretation of model coefficients. See the negative beta for Publication Year. The higher the publication year, the lower the variance (better precision), controlling for H5.

Scatterplot year <-> precision

Size of the points indicate the H5 index (the bigger the higher) of the journal that the ES is published in.

Citations

Linear mixed-effects model. Taking into effect clustering of ESs due to originating from the same study. Using square root of variance to make the distribution normal.

##                                      Estimate Std. Error       df     t value
## (Intercept)                      -0.201059160 0.09182019 84.97688 -2.18970532
## scale(publicationYear)           -0.003047723 0.11144001 85.08921 -0.02734855
## scale(h5indexGSJournalMarch2016) -0.365198106 0.11472691 84.73581 -3.18319482
## scale(citationsGSMarch2016)       0.332488656 0.10530882 85.10522  3.15727251
##                                     Pr(>|t|)
## (Intercept)                      0.031286371
## scale(publicationYear)           0.978245772
## scale(h5indexGSJournalMarch2016) 0.002037484
## scale(citationsGSMarch2016)      0.002203503

Scatterplot precision <-> citations

The relationship between precision (sqrt of variance) and number of citations.

## `geom_smooth()` using formula 'y ~ x'

H5 index

Linear mixed-effects model. Taking into effect clustering of ESs due to originating from the same study. Using square root of variance to make the distribution normal.

##                                    Estimate Std. Error       df   t value
## (Intercept)                      -0.1141591  0.0968350 87.04277 -1.178903
## scale(h5indexGSJournalMarch2016) -0.1178194  0.1027219 86.98377 -1.146974
##                                   Pr(>|t|)
## (Intercept)                      0.2416493
## scale(h5indexGSJournalMarch2016) 0.2545376

Scatterplot precision <-> journal H5

The relationship between precision (sqrt of variance) and H5 index of the journal.

Decline effect

Linear mixed-effects model. Taking into effect clustering of ESs due to originating from the same study.

##                           Estimate Std. Error       df  t value      Pr(>|t|)
## (Intercept)            0.001256607 0.08446073 86.44516 0.014878 0.98816379458
## scale(sqrt(vi))        0.361986905 0.08744592 79.13890 4.139552 0.00008628732
## scale(publicationYear) 0.114376928 0.08487961 97.53930 1.347519 0.18093479180

Citation bias

Do more highly-cited studies report larger effect sizes?

##                                     Estimate Std. Error       df    t value
## (Intercept)                       0.22906790 0.01409808 76.12334 16.2481604
## scale(publicationYear)           -0.01667034 0.01740063 82.82924 -0.9580309
## scale(h5indexGSJournalMarch2016) -0.04319160 0.01701388 64.19127 -2.5386092
## scale(citationsGSMarch2016)       0.02599613 0.01651275 85.24009  1.5743067
##                                                            Pr(>|t|)
## (Intercept)                      0.00000000000000000000000001855457
## scale(publicationYear)           0.34083527493986021106309181050165
## scale(h5indexGSJournalMarch2016) 0.01356484913606161198107447063421
## scale(citationsGSMarch2016)      0.11912106790969252678724643601527

P-curve for interaction effects

## P-curve analysis 
##  ----------------------- 
## - Total number of provided studies: k = 51 
## - Total number of p<0.05 studies included into the analysis: k = 37 (72.55%) 
## - Total number of studies with p<0.025: k = 22 (43.14%) 
##    
## Results 
##  ----------------------- 
##                     pBinomial  zFull pFull  zHalf pHalf
## Right-skewness test     0.162 -4.955 0.000 -6.205     0
## Flatness test           0.080  0.988 0.838  7.763     1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.   
## Power Estimate: 45% (25.8%-64.1%)
##    
## Evidential value 
##  ----------------------- 
## - Evidential value present: yes 
## - Evidential value absent/inadequate: no

Counts

Simple counts: 1. How often did authors test for moderation by attachment?

## 
##   0   1 
## 303  19
  1. How often did authors use which attachment measure?
## 
##                                                                                                                  
##                                                                                                              321 
##                                                                                                                0 
##                                                                                                                2 
##                                                                             affiliation FNE scale  (Leary, 1983) 
##                                                                                                                1 
## affiliation.  Multi-Motive Grid (MMG) (Schmalt et al. 2000, for the English version, see Sokolowski et al. 2000) 
##                                                                                                                1 
##                                                         affiliation. five-item measure of Park and Maner (2009), 
##                                                                                                                1 
##                                                                        derived from Bartholomew & Horowitz, 1991 
##                                                                                                                1 
##                                                                                 Fraley, Waller, & Brennan (2000) 
##                                                                                                               14 
##                                                                                  Fraley, Waller, & Brennan, 2000 
##                                                                                                                1 
##                                                                        Need For Affiliation (Park & Maner, 2009) 
##                                                                                                                1 
##                                                                        Wei, Russell, Mallinckrodt, & Vogel, 2007 
##                                                                                                                3
  1. Via validated tests: How often was tested for:
  1. Independence of awareness
## 
##   0 
## 323
  1. Lack of intention
## 
##   0 
## 320
  1. Efficiency of behavior
## 
##   0 
## 317
  1. Lack of control of behavior
## 
##   0 
## 321
  1. Via non-validated tests: How often was tested for:
  1. Independence of awareness
## 
##         0         1 
## 0.7080925 0.2167630
## [1] 51
  1. Lack of intention
## 
##   0 
## 323
  1. Efficiency of behavior
## 
##   0 
## 320
  1. Lack of control of behavior
## 
##   0 
## 322
  1. Moderation by measures assessing attachment other than by self-disclosure of emotions
## < table of extent 0 >
  1. Whether researchers tested for innateness
## 
##   0 
## 320
  1. Has skin color/ethnicity been reported?
## < table of extent 0 >
  1. Skin temperature location
## 
## 1 2 
## 6 5
  1. Population type
## 
##         general special student 
##      28      84       8     226
  1. Control group type (1 = active controls)
## 
##  0  1 
##  6 37
  1. In how many countries the studies were conducted and what their mean distance was from the equator
##  [1] "USA"         ""            "China"       "Portugal"    "Singapore"  
##  [6] "Israel"      "Germany"     "South Korea" "Netherlands" "Japan"      
## [11] "Scotland"    "England"     "India"       "Canada"      "Switzerland"
## [16] "Poland"      "Italy"

Number of independent studies

## [1] 84

Number of papers

## [1] 33

Lattitude

## Lattitude mean   Lattitude SD            Min            Max 
##       39.74729       10.84424        1.29686       57.16498